In this enlightening podcast episode, we delve into the world of stream processing and cloud-native solutions with two key figures from Quix: Michael Rosam, Founder and CEO, and Tun Shwe, VP of Data and DevRel.
Quix Streams is a game-changing cloud-native solution that harnesses the agility of Python for stream processing, all wrapped up in a lightweight library. The conversation with Michael and Tun sheds light on Quix's journey, motivations, and the pivotal role it plays in the data processing landscape.
Michael shares his motivation for founding Quix, stemming from his experiences in F1 racing that demanded true real-time capabilities. Quix's goal: take humans out of the equation and embrace automation, all while ensuring true real-time data processing.
Tun, holding dual VP roles, provides insight into the intersection of these roles. Education is the common thread, and the overlap arises from content creation and the shared mission of selling the dream.
But why Python? The founders' choice to go open source in February this year is rooted in a belief in the community and the desire to empower users to explore the full potential of stream processing.
Do data scientists need to be well-versed in streaming? Michael and Tun concur that a basic understanding is crucial, focusing on state management and checkpointing.
Looking ahead, the future of Quix promises exciting developments, including streaming data frames reminiscent of Pandas and compatibility with scikit-learn. The podcast concludes with insights into where you can find Quix and what's on the horizon.
Don't miss this captivating episode as Quix accelerates through the data processing landscape at speeds up to 300 mph, where the journey is a lot like an F1 race, and the major launch of Quix V2 is on the horizon.
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